Generative Method to Discover Genetically Driven Image Biomarkers
We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Springer-Verlag
2017
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| Online Access: | http://hdl.handle.net/1721.1/111020 https://orcid.org/0000-0002-1164-0500 https://orcid.org/0000-0002-4616-8250 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1811085938821955584 |
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| author | Cho, Michael Estepar, Raul San Jose Batmanghelich, Nematollah Saeedi, Ardavan Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Cho, Michael Estepar, Raul San Jose Batmanghelich, Nematollah Saeedi, Ardavan Golland, Polina |
| author_sort | Cho, Michael |
| collection | MIT |
| description | We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pulmonary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort. |
| first_indexed | 2024-09-23T13:17:49Z |
| format | Article |
| id | mit-1721.1/111020 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:17:49Z |
| publishDate | 2017 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/1110202022-10-01T14:24:21Z Generative Method to Discover Genetically Driven Image Biomarkers Cho, Michael Estepar, Raul San Jose Batmanghelich, Nematollah Saeedi, Ardavan Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Batmanghelich, Nematollah Saeedi, Ardavan Golland, Polina We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pulmonary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort. National Institute of Biomedical Imaging and Bioengineering (U.S.) (U54-EB005149) National Institutes of Health (U.S.) (P41-RR13218) National Institute of Biomedical Imaging and Bioengineering (U.S.) (P41-EB015902) National Heart, Lung, and Blood Institute (R01HL089856) National Heart, Lung, and Blood Institute (R01HL089897) National Heart, Lung, and Blood Institute (K08HL097029) National Heart, Lung, and Blood Institute (R01HL113264) National Heart, Lung, and Blood Institute (5K25HL104085) National Heart, Lung, and Blood Institute (5R01HL116931) National Heart, Lung, and Blood Institute (5R01HL116473) 2017-08-24T20:21:38Z 2017-08-24T20:21:38Z 2015-06 Article http://purl.org/eprint/type/ConferencePaper 978-3-319-19991-7 978-3-319-19992-4 0302-9743 1611-3349 http://hdl.handle.net/1721.1/111020 Batmanghelich, Nematollah K. et al. “Generative Method to Discover Genetically Driven Image Biomarkers.”Ourselin S., Alexander D., Westin CF., Cardoso M., editors. Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science, 9123 (2015): 30–42. © 2015 Springer International Publishing Switzerland https://orcid.org/0000-0002-1164-0500 https://orcid.org/0000-0002-4616-8250 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1007/978-3-319-19992-4_3 Information Processing in Medical Imaging Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer-Verlag PMC |
| spellingShingle | Cho, Michael Estepar, Raul San Jose Batmanghelich, Nematollah Saeedi, Ardavan Golland, Polina Generative Method to Discover Genetically Driven Image Biomarkers |
| title | Generative Method to Discover Genetically Driven Image Biomarkers |
| title_full | Generative Method to Discover Genetically Driven Image Biomarkers |
| title_fullStr | Generative Method to Discover Genetically Driven Image Biomarkers |
| title_full_unstemmed | Generative Method to Discover Genetically Driven Image Biomarkers |
| title_short | Generative Method to Discover Genetically Driven Image Biomarkers |
| title_sort | generative method to discover genetically driven image biomarkers |
| url | http://hdl.handle.net/1721.1/111020 https://orcid.org/0000-0002-1164-0500 https://orcid.org/0000-0002-4616-8250 https://orcid.org/0000-0003-2516-731X |
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